Kaggle Facial Points Detection: Univ of Montreal: left_eye_center_x regression:: Faces_tmplt
bdanalytics
Date: (Tue) Jan 05, 2016
Introduction:
Data: Source: Training: https://www.kaggle.com/c/facial-keypoints-detection/download/training.zip
New: https://www.kaggle.com/c/facial-keypoints-detection/download/test.zip
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Prediction Accuracy Enhancement Options:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glbFeatsText
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Add n-grams for glbFeatsText
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<pointer>"; if url specifies a zip file, name = "<filename>
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://www.kaggle.com/c/facial-keypoints-detection/download/training.zip",
name = "training/training.csv")
glbObsNewFile <- list(url = "https://www.kaggle.com/c/facial-keypoints-detection/download/test.zip",
name = "test/test.csv") # default OR
#list(splitSpecs = list(method = NULL #select from c(NULL, "condition", "sample", "copy")
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
# )
glbInpMerge <- NULL #: default
# list(fnames = c("<fname1>", "<fname2>")) # files will be concatenated
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # FALSE not supported - use "copy" for glbObsNewFile$splitSpecs$method # select from c(FALSE, TRUE)
glbObsDropCondition <- #NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
'(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- NULL # or TRUE or FALSE
glb_rsp_var_raw <- "left_eye_center_x"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw # or "left_eye_center_x.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL
# function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
# }
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, names(table(glbObsAll[, glb_rsp_var_raw]))))
glb_map_rsp_var_to_raw <- NULL
# function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
# }
# glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "ImageId" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- NULL # choose from c(NULL : default, "<category_feat>")
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category & work each one in
# ,"left_eye_center_x"
,"left_eye_center_y"
,"right_eye_center_x", "right_eye_center_y"
,"left_eye_inner_corner_x", "left_eye_inner_corner_y"
,"left_eye_outer_corner_x", "left_eye_outer_corner_y"
,"right_eye_inner_corner_x", "right_eye_inner_corner_y"
,"right_eye_outer_corner_x", "right_eye_outer_corner_y"
,"left_eyebrow_inner_end_x", "left_eyebrow_inner_end_y"
,"left_eyebrow_outer_end_x", "left_eyebrow_outer_end_y"
,"right_eyebrow_inner_end_x", "right_eyebrow_inner_end_y"
,"right_eyebrow_outer_end_x", "right_eyebrow_outer_end_y"
,"nose_tip_x", "nose_tip_y"
,"mouth_left_corner_x", "mouth_left_corner_y"
,"mouth_right_corner_x", "mouth_right_corner_y"
,"mouth_center_top_lip_x", "mouth_center_top_lip_y"
,"mouth_center_bottom_lip_x", "mouth_center_bottom_lip_y"
,"Image"
)
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
,"Image"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(.rnorm) { return(1:length(.rnorm)) }
, args = c(".rnorm"))
glbFeatsDerive[["ImageId"]] <- list(
mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d", .pos), sep = "#"))
return(paste(.src, sprintf("%04d",
ifelse(.src == "Train", .pos, .pos - 7049)
), sep = "#"))
}
, args = c(".src", ".pos"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
glb_derive_vars <- names(glbFeatsDerive)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S", timezone = "America/New_York", impute.na = TRUE,
# last.ctg = TRUE, poly.ctg = TRUE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["<mdlId>"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indep_vars, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glb_sel_mdl_id <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c(glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. glbFeatsId, glbFeatsCategory & glb_rsp_var
)
# Output specs
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
glb_out_vars_lst <- list()
# glbFeatsId will be the first output column, by default
if (glb_is_classification && glb_is_binomial) {
glb_out_vars_lst[["Probability1"]] <-
"%<d-% mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$prob"
} else {
glb_out_vars_lst[[glb_rsp_var]] <-
"%<d-% mygetPredictIds(glb_rsp_var, glb_fin_mdl_id)$value"
}
# glb_out_vars_lst[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glb_out_vars_lst[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glb_out_pfx <- "Faces_tmplt_"
glb_save_envir <- FALSE # or TRUE
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 10.714 NA NAStep 1.0: import data
chunk option: eval=
## [1] "Reading file ./data/training/training.csv..."
## [1] "dimensions of data in ./data/training/training.csv: 7,049 rows x 31 cols"
## [1] " Truncating Image to first 100 chars..."
## left_eye_center_x left_eye_center_y right_eye_center_x
## 1 66.03356 39.00227 30.22701
## 2 64.33294 34.97008 29.94928
## 3 65.05705 34.90964 30.90379
## 4 65.22574 37.26177 32.02310
## 5 66.72530 39.62126 32.24481
## 6 69.68075 39.96875 29.18355
## right_eye_center_y left_eye_inner_corner_x left_eye_inner_corner_y
## 1 36.42168 59.58208 39.64742
## 2 33.44871 58.85617 35.27435
## 3 34.90964 59.41200 36.32097
## 4 37.26177 60.00334 39.12718
## 5 38.04203 58.56589 39.62126
## 6 37.56336 62.86430 40.16927
## left_eye_outer_corner_x left_eye_outer_corner_y right_eye_inner_corner_x
## 1 73.13035 39.97000 36.35657
## 2 70.72272 36.18717 36.03472
## 3 70.98442 36.32097 37.67811
## 4 72.31471 38.38097 37.61864
## 5 72.51593 39.88447 36.98238
## 6 76.89824 41.17189 36.40105
## right_eye_inner_corner_y right_eye_outer_corner_x
## 1 37.38940 23.45287
## 2 34.36153 24.47251
## 3 36.32097 24.97642
## 4 38.75411 25.30727
## 5 39.09485 22.50611
## 6 39.36763 21.76553
## right_eye_outer_corner_y left_eyebrow_inner_end_x
## 1 37.38940 56.95326
## 2 33.14444 53.98740
## 3 36.60322 55.74253
## 4 38.00790 56.43381
## 5 38.30524 57.24957
## 6 38.56553 59.76628
## left_eyebrow_inner_end_y left_eyebrow_outer_end_x
## 1 29.03365 80.22713
## 2 28.27595 78.63421
## 3 27.57095 78.88737
## 4 30.92986 77.91026
## 5 30.67218 77.76294
## 6 31.65129 83.31364
## left_eyebrow_outer_end_y right_eyebrow_inner_end_x
## 1 32.22814 40.22761
## 2 30.40592 42.72885
## 3 32.65162 42.19389
## 4 31.66573 41.67151
## 5 31.73725 38.03544
## 6 35.35806 39.40800
## right_eyebrow_inner_end_y right_eyebrow_outer_end_x
## 1 29.00232 16.35638
## 2 26.14604 16.86536
## 3 28.13545 16.79116
## 4 31.04999 20.45802
## 5 30.93538 15.92587
## 6 30.54639 14.94908
## right_eyebrow_outer_end_y nose_tip_x nose_tip_y mouth_left_corner_x
## 1 29.64747 44.42057 57.06680 61.19531
## 2 27.05886 48.20630 55.66094 56.42145
## 3 32.08712 47.55726 53.53895 60.82295
## 4 29.90934 51.88508 54.16654 65.59889
## 5 30.67218 43.29953 64.88952 60.67141
## 6 32.15013 52.46849 58.80000 64.86908
## mouth_left_corner_y mouth_right_corner_x mouth_right_corner_y
## 1 79.97017 28.61450 77.38899
## 2 76.35200 35.12238 76.04766
## 3 73.01432 33.72632 72.73200
## 4 72.70372 37.24550 74.19548
## 5 77.52324 31.19175 76.99730
## 6 82.47118 31.99043 81.66908
## mouth_center_top_lip_x mouth_center_top_lip_y mouth_center_bottom_lip_x
## 1 43.31260 72.93546 43.13071
## 2 46.68460 70.26655 45.46791
## 3 47.27495 70.19179 47.27495
## 4 50.30317 70.09169 51.56118
## 5 44.96275 73.70739 44.22714
## 6 49.30811 78.48763 49.43237
## mouth_center_bottom_lip_y
## 1 84.48577
## 2 85.48017
## 3 78.65937
## 4 78.26838
## 5 86.87117
## 6 93.89877
## Image
## 1 238 236 237 238 240 240 239 241 241 243 240 239 231 212 190 173 148 122 104 92 79 73 74 73 73 74 81
## 2 219 215 204 196 204 211 212 200 180 168 178 196 194 196 203 209 199 192 197 201 207 215 199 190 182
## 3 144 142 159 180 188 188 184 180 167 132 84 59 54 57 62 61 55 54 56 50 60 78 85 86 88 89 90 90 88 89
## 4 193 192 193 194 194 194 193 192 168 111 50 12 1 1 1 1 1 1 1 1 1 1 6 16 19 17 13 13 16 22 25 31 34 27
## 5 147 148 160 196 215 214 216 217 219 220 206 188 166 104 88 81 77 71 63 58 58 52 58 62 59 60 55 51 57
## 6 167 169 170 167 156 145 106 68 52 24 20 15 21 14 6 9 11 11 29 49 61 71 76 80 82 84 84 84 83 88 91 92
## left_eye_center_x left_eye_center_y right_eye_center_x
## 244 63.76497 38.17976 24.46709
## 1074 67.61736 35.73515 32.32068
## 3590 68.68139 35.63807 30.43863
## 5129 65.56684 37.63803 33.38537
## 5183 68.42459 43.83568 35.01755
## 6975 47.85052 37.39213 26.10544
## right_eye_center_y left_eye_inner_corner_x left_eye_inner_corner_y
## 244 40.70400 57.27570 38.90097
## 1074 36.64797 61.53191 36.64797
## 3590 38.30054 NA NA
## 5129 38.01223 NA NA
## 5183 39.69364 NA NA
## 6975 40.37090 NA NA
## left_eye_outer_corner_x left_eye_outer_corner_y
## 244 71.69667 38.90097
## 1074 73.70281 35.43088
## 3590 NA NA
## 5129 NA NA
## 5183 NA NA
## 6975 NA NA
## right_eye_inner_corner_x right_eye_inner_corner_y
## 244 31.31697 39.98279
## 1074 38.71047 37.25651
## 3590 NA NA
## 5129 NA NA
## 5183 NA NA
## 6975 NA NA
## right_eye_outer_corner_x right_eye_outer_corner_y
## 244 17.25661 41.78501
## 1074 25.62655 36.95224
## 3590 NA NA
## 5129 NA NA
## 5183 NA NA
## 6975 NA NA
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 244 54.39086 29.52766
## 1074 59.73991 31.37784
## 3590 NA NA
## 5129 NA NA
## 5183 NA NA
## 6975 NA NA
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 244 76.74434 27.36403
## 1074 77.04987 28.43248
## 3590 NA NA
## 5129 NA NA
## 5183 NA NA
## 6975 NA NA
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 244 33.84121 31.69049
## 1074 43.01889 32.12017
## 3590 NA NA
## 5129 NA NA
## 5183 NA NA
## 6975 NA NA
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y nose_tip_x
## 244 11.84834 33.49351 43.93573
## 1074 21.97583 32.08381 51.18638
## 3590 NA NA 51.01229
## 5129 NA NA 49.47608
## 5183 NA NA 51.17158
## 6975 NA NA 33.25451
## nose_tip_y mouth_left_corner_x mouth_left_corner_y
## 244 52.96215 60.52034 76.75644
## 1074 57.33855 66.40000 72.24851
## 3590 59.60032 NA NA
## 5129 63.08383 NA NA
## 5183 64.96020 NA NA
## 6975 59.13721 NA NA
## mouth_right_corner_x mouth_right_corner_y mouth_center_top_lip_x
## 244 35.64343 78.55946 46.81976
## 1074 36.27643 72.55285 51.49072
## 3590 NA NA NA
## 5129 NA NA NA
## 5183 NA NA NA
## 6975 NA NA NA
## mouth_center_top_lip_y mouth_center_bottom_lip_x
## 244 70.62776 47.90158
## 1074 72.24851 51.49072
## 3590 NA 52.46453
## 5129 NA 49.32317
## 5183 NA 49.51476
## 6975 NA 38.31844
## mouth_center_bottom_lip_y
## 244 83.96773
## 1074 80.76800
## 3590 67.58770
## 5129 72.81309
## 5183 75.72958
## 6975 76.71200
## Image
## 244 41 36 34 33 41 47 43 38 37 39 40 35 27 23 27 31 32 28 26 29 35 38 37 39 42 41 40 42 41 39 44 50 51 4
## 1074 202 201 202 202 201 201 201 201 184 96 36 30 30 35 41 54 95 158 191 194 194 195 195 193 191 188 189
## 3590 219 219 217 220 228 225 223 223 224 224 226 226 227 223 218 220 225 224 220 207 206 208 203 211 220
## 5129 194 196 197 198 197 194 192 188 189 196 108 53 69 51 48 35 34 19 33 45 31 17 25 15 12 19 23 27 29 29
## 5183 140 137 127 118 111 104 105 111 115 116 117 111 104 99 93 90 93 96 93 90 91 95 101 114 126 137 150 1
## 6975 31 28 27 31 38 46 62 70 82 90 93 92 88 85 83 75 71 65 57 50 41 33 28 24 23 25 28 32 35 38 38 36 32 2
## left_eye_center_x left_eye_center_y right_eye_center_x
## 7044 66.86722 37.35686 30.75093
## 7045 67.40255 31.84255 29.74675
## 7046 66.13440 38.36550 30.47863
## 7047 66.69073 36.84522 31.66642
## 7048 70.96508 39.85367 30.54328
## 7049 66.93831 43.42451 31.09606
## right_eye_center_y left_eye_inner_corner_x left_eye_inner_corner_y
## 7044 40.11574 NA NA
## 7045 38.63294 NA NA
## 7046 39.95020 NA NA
## 7047 39.68504 NA NA
## 7048 40.77234 NA NA
## 7049 39.52860 NA NA
## left_eye_outer_corner_x left_eye_outer_corner_y
## 7044 NA NA
## 7045 NA NA
## 7046 NA NA
## 7047 NA NA
## 7048 NA NA
## 7049 NA NA
## right_eye_inner_corner_x right_eye_inner_corner_y
## 7044 NA NA
## 7045 NA NA
## 7046 NA NA
## 7047 NA NA
## 7048 NA NA
## 7049 NA NA
## right_eye_outer_corner_x right_eye_outer_corner_y
## 7044 NA NA
## 7045 NA NA
## 7046 NA NA
## 7047 NA NA
## 7048 NA NA
## 7049 NA NA
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 7044 NA NA
## 7045 NA NA
## 7046 NA NA
## 7047 NA NA
## 7048 NA NA
## 7049 NA NA
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 7044 NA NA
## 7045 NA NA
## 7046 NA NA
## 7047 NA NA
## 7048 NA NA
## 7049 NA NA
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 7044 NA NA
## 7045 NA NA
## 7046 NA NA
## 7047 NA NA
## 7048 NA NA
## 7049 NA NA
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y nose_tip_x
## 7044 NA NA 43.54211
## 7045 NA NA 48.26596
## 7046 NA NA 47.91035
## 7047 NA NA 49.46257
## 7048 NA NA 50.75420
## 7049 NA NA 47.06925
## nose_tip_y mouth_left_corner_x mouth_left_corner_y
## 7044 64.94569 NA NA
## 7045 67.02909 NA NA
## 7046 66.62601 NA NA
## 7047 67.51516 NA NA
## 7048 66.72499 NA NA
## 7049 73.03334 NA NA
## mouth_right_corner_x mouth_right_corner_y mouth_center_top_lip_x
## 7044 NA NA NA
## 7045 NA NA NA
## 7046 NA NA NA
## 7047 NA NA NA
## 7048 NA NA NA
## 7049 NA NA NA
## mouth_center_top_lip_y mouth_center_bottom_lip_x
## 7044 NA 47.55504
## 7045 NA 50.42664
## 7046 NA 50.28740
## 7047 NA 49.46257
## 7048 NA 50.06519
## 7049 NA 45.90048
## mouth_center_bottom_lip_y
## 7044 79.49255
## 7045 79.68392
## 7046 77.98302
## 7047 78.11712
## 7048 79.58645
## 7049 82.77310
## Image
## 7044 150 150 132 63 44 74 86 61 62 57 44 70 93 115 114 115 99 110 94 108 108 94 97 86 79 75 101 90 93 89
## 7045 71 74 85 105 116 128 139 150 170 187 201 209 218 219 212 198 184 181 185 188 193 196 199 202 206 208
## 7046 60 60 62 57 55 51 49 48 50 53 56 56 106 89 77 98 100 107 106 90 90 94 88 94 103 118 123 126 123 144
## 7047 74 74 74 78 79 79 79 81 77 78 80 73 72 81 77 120 184 191 193 172 194 203 203 202 198 199 207 214 214
## 7048 254 254 254 254 254 238 193 145 121 118 119 109 106 106 105 107 109 111 113 117 126 129 129 129 129
## 7049 53 62 67 76 86 91 97 105 105 106 107 108 112 117 123 129 130 128 132 134 136 142 149 155 157 157 153
## 'data.frame': 7049 obs. of 20 variables:
## $ left_eye_center_x : num 66 64.3 65.1 65.2 66.7 ...
## $ left_eye_center_y : num 39 35 34.9 37.3 39.6 ...
## $ right_eye_center_x : num 30.2 29.9 30.9 32 32.2 ...
## $ right_eye_center_y : num 36.4 33.4 34.9 37.3 38 ...
## $ left_eye_inner_corner_x : num 59.6 58.9 59.4 60 58.6 ...
## $ left_eye_inner_corner_y : num 39.6 35.3 36.3 39.1 39.6 ...
## $ left_eye_outer_corner_x : num 73.1 70.7 71 72.3 72.5 ...
## $ left_eye_outer_corner_y : num 40 36.2 36.3 38.4 39.9 ...
## $ right_eye_inner_corner_x : num 36.4 36 37.7 37.6 37 ...
## $ right_eye_inner_corner_y : num 37.4 34.4 36.3 38.8 39.1 ...
## $ right_eye_outer_corner_x : num 23.5 24.5 25 25.3 22.5 ...
## $ right_eye_outer_corner_y : num 37.4 33.1 36.6 38 38.3 ...
## $ left_eyebrow_inner_end_x : num 57 54 55.7 56.4 57.2 ...
## $ left_eyebrow_inner_end_y : num 29 28.3 27.6 30.9 30.7 ...
## $ left_eyebrow_outer_end_x : num 80.2 78.6 78.9 77.9 77.8 ...
## $ left_eyebrow_outer_end_y : num 32.2 30.4 32.7 31.7 31.7 ...
## $ right_eyebrow_inner_end_x: num 40.2 42.7 42.2 41.7 38 ...
## $ right_eyebrow_inner_end_y: num 29 26.1 28.1 31 30.9 ...
## $ right_eyebrow_outer_end_x: num 16.4 16.9 16.8 20.5 15.9 ...
## $ right_eyebrow_outer_end_y: num 29.6 27.1 32.1 29.9 30.7 ...
## NULL
## 'data.frame': 7049 obs. of 21 variables:
## $ right_eye_outer_corner_x : num 23.5 24.5 25 25.3 22.5 ...
## $ right_eye_outer_corner_y : num 37.4 33.1 36.6 38 38.3 ...
## $ left_eyebrow_inner_end_x : num 57 54 55.7 56.4 57.2 ...
## $ left_eyebrow_inner_end_y : num 29 28.3 27.6 30.9 30.7 ...
## $ left_eyebrow_outer_end_x : num 80.2 78.6 78.9 77.9 77.8 ...
## $ left_eyebrow_outer_end_y : num 32.2 30.4 32.7 31.7 31.7 ...
## $ right_eyebrow_inner_end_x: num 40.2 42.7 42.2 41.7 38 ...
## $ right_eyebrow_inner_end_y: num 29 26.1 28.1 31 30.9 ...
## $ right_eyebrow_outer_end_x: num 16.4 16.9 16.8 20.5 15.9 ...
## $ right_eyebrow_outer_end_y: num 29.6 27.1 32.1 29.9 30.7 ...
## $ nose_tip_x : num 44.4 48.2 47.6 51.9 43.3 ...
## $ nose_tip_y : num 57.1 55.7 53.5 54.2 64.9 ...
## $ mouth_left_corner_x : num 61.2 56.4 60.8 65.6 60.7 ...
## $ mouth_left_corner_y : num 80 76.4 73 72.7 77.5 ...
## $ mouth_right_corner_x : num 28.6 35.1 33.7 37.2 31.2 ...
## $ mouth_right_corner_y : num 77.4 76 72.7 74.2 77 ...
## $ mouth_center_top_lip_x : num 43.3 46.7 47.3 50.3 45 ...
## $ mouth_center_top_lip_y : num 72.9 70.3 70.2 70.1 73.7 ...
## $ mouth_center_bottom_lip_x: num 43.1 45.5 47.3 51.6 44.2 ...
## $ mouth_center_bottom_lip_y: num 84.5 85.5 78.7 78.3 86.9 ...
## $ Image : chr "238 236 237 238 240 240 239 241 241 243 240 239 231 212 190 173 148 122 104 92 79 73 74 73 73 74 81 74 60 64 75 86 93 102 100 1"| __truncated__ "219 215 204 196 204 211 212 200 180 168 178 196 194 196 203 209 199 192 197 201 207 215 199 190 182 180 183 190 190 176 175 175"| __truncated__ "144 142 159 180 188 188 184 180 167 132 84 59 54 57 62 61 55 54 56 50 60 78 85 86 88 89 90 90 88 89 91 94 95 98 99 101 104 107 "| __truncated__ "193 192 193 194 194 194 193 192 168 111 50 12 1 1 1 1 1 1 1 1 1 1 6 16 19 17 13 13 16 22 25 31 34 27 15 19 16 19 17 13 9 6 3 1 "| __truncated__ ...
## NULL## Warning in myprint_str_df(df): [list output truncated]## [1] "Reading file ./data/test/test.csv..."
## [1] "dimensions of data in ./data/test/test.csv: 1,783 rows x 2 cols"
## [1] " Truncating Image to first 100 chars..."
## ImageId
## 1 1
## 2 2
## 3 3
## 4 4
## 5 5
## 6 6
## Image
## 1 182 183 182 182 180 180 176 169 156 137 124 103 79 62 54 56 58 48 49 45 39 37 42 43 52 61 78 93 104
## 2 76 87 81 72 65 59 64 76 69 42 31 38 49 58 58 47 37 33 32 33 35 50 55 54 50 51 61 78 92 100 101 79 55
## 3 177 176 174 170 169 169 168 166 166 166 161 140 69 5 1 2 1 18 61 96 110 122 129 129 127 125 125 119
## 4 176 174 174 175 174 174 176 176 175 171 165 157 143 134 134 137 138 137 135 135 134 137 135 128 128
## 5 50 47 44 101 144 149 120 58 48 42 35 35 37 39 38 36 34 31 31 32 32 34 34 34 35 33 32 30 31 33 33 31
## 6 177 177 177 171 142 115 97 84 89 90 88 82 63 51 40 35 39 37 42 38 29 35 43 64 95 117 127 115 108 125
## ImageId
## 3 3
## 319 319
## 691 691
## 698 698
## 717 717
## 824 824
## Image
## 3 177 176 174 170 169 169 168 166 166 166 161 140 69 5 1 2 1 18 61 96 110 122 129 129 127 125 125 119
## 319 33 34 38 39 37 32 29 26 24 24 24 23 26 46 65 68 73 77 90 99 100 107 111 113 117 121 128 138 148 154
## 691 34 32 34 43 38 23 8 15 18 19 19 39 47 45 30 43 51 50 44 40 37 36 37 37 37 39 41 43 48 50 53 57 59 62
## 698 14 14 15 16 18 21 23 25 27 29 30 31 31 33 34 36 39 45 60 73 81 89 97 108 115 121 126 128 129 127 124
## 717 17 14 21 20 17 40 77 93 103 121 150 165 153 144 144 118 90 112 132 155 167 170 172 176 181 179 182 1
## 824 86 110 151 194 223 197 177 158 149 144 181 207 216 206 185 163 142 128 117 109 83 53 54 57 63 71 80
## ImageId
## 1778 1778
## 1779 1779
## 1780 1780
## 1781 1781
## 1782 1782
## 1783 1783
## Image
## 1778 100 106 105 106 105 104 104 108 112 114 111 108 108 111 113 111 108 117 130 114 114 135 108 87 91 82
## 1779 101 101 101 100 100 97 97 98 102 149 214 206 171 159 159 162 170 178 171 171 171 171 170 164 163 175
## 1780 201 191 171 158 145 140 136 130 123 115 108 104 100 96 99 115 132 155 167 174 170 160 159 158 166 17
## 1781 28 28 29 30 31 32 33 34 39 44 46 46 49 54 61 73 84 97 110 119 128 133 137 138 139 140 144 146 147 14
## 1782 104 95 71 57 46 52 65 70 70 67 76 72 69 69 72 75 73 68 81 67 58 35 33 41 27 20 13 28 39 53 70 75 80
## 1783 63 61 64 66 66 64 65 70 69 70 77 83 63 34 22 21 21 18 23 12 17 22 24 37 32 15 15 20 20 15 9 9 9 8 9
## 'data.frame': 1783 obs. of 2 variables:
## $ ImageId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ Image : chr "182 183 182 182 180 180 176 169 156 137 124 103 79 62 54 56 58 48 49 45 39 37 42 43 52 61 78 93 104 107 114 115 117 122 120 122"| __truncated__ "76 87 81 72 65 59 64 76 69 42 31 38 49 58 58 47 37 33 32 33 35 50 55 54 50 51 61 78 92 100 101 79 55 47 52 50 47 39 38 52 46 25"| __truncated__ "177 176 174 170 169 169 168 166 166 166 161 140 69 5 1 2 1 18 61 96 110 122 129 129 127 125 125 119 112 110 111 107 102 102 99 "| __truncated__ "176 174 174 175 174 174 176 176 175 171 165 157 143 134 134 137 138 137 135 135 134 137 135 128 128 129 122 110 107 112 115 123"| __truncated__ ...
## - attr(*, "comment")= chr "glbObsNew"
## NULL## Warning: dropping vars: Image## [1] "Creating new feature: .pos..."
## [1] "Creating new feature: ImageId..."## [1] "Partition stats:"## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## left_eye_center_x.cut.fctr .src .n
## 1 (46.7,70.7] Train 6652
## 2 <NA> Test 1783
## 3 (70.7,94.8] Train 342
## 4 (22.7,46.7] Train 45
## 5 <NA> Train 10
## left_eye_center_x.cut.fctr .src .n
## 1 (46.7,70.7] Train 6652
## 2 <NA> Test 1783
## 3 (70.7,94.8] Train 342
## 4 (22.7,46.7] Train 45
## 5 <NA> Train 10
## .src .n
## 1 Train 7049
## 2 Test 1783## [1] "Running glbObsDropCondition filter: (is.na(glbObsAll[, glb_rsp_var_raw]) & grepl(\"Train\", glbObsAll[, glbFeatsId]))"
## [1] "Partition stats:"
## left_eye_center_x.cut.fctr .src .n
## 1 (46.7,70.7] Train 6652
## 2 <NA> Test 1783
## 3 (70.7,94.8] Train 342
## 4 (22.7,46.7] Train 45
## left_eye_center_x.cut.fctr .src .n
## 1 (46.7,70.7] Train 6652
## 2 <NA> Test 1783
## 3 (70.7,94.8] Train 342
## 4 (22.7,46.7] Train 45
## .src .n
## 1 Train 7039
## 2 Test 1783## Loading required package: lazyeval
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size## [1] "Found 0 duplicates by all features:"## NULL## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 10.714 82.897 72.183
## 2 inspect.data 2 0 0 82.897 NA NAStep 2.0: inspect data
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 1783 rows containing non-finite values (stat_bin).
## [1] "numeric data missing in : "
## left_eye_center_x left_eye_center_y
## 1783 1783
## right_eye_center_x right_eye_center_y
## 1789 1789
## left_eye_inner_corner_x left_eye_inner_corner_y
## 6556 6556
## left_eye_outer_corner_x left_eye_outer_corner_y
## 6559 6559
## right_eye_inner_corner_x right_eye_inner_corner_y
## 6559 6559
## right_eye_outer_corner_x right_eye_outer_corner_y
## 6559 6559
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 6557 6557
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 6603 6603
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 6561 6561
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y
## 6594 6594
## nose_tip_x nose_tip_y
## 1783 1783
## mouth_left_corner_x mouth_left_corner_y
## 6562 6562
## mouth_right_corner_x mouth_right_corner_y
## 6561 6561
## mouth_center_top_lip_x mouth_center_top_lip_y
## 6557 6557
## mouth_center_bottom_lip_x mouth_center_bottom_lip_y
## 1816 1816
## [1] "numeric data w/ 0s in : "
## named integer(0)
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## ImageId
## 0
## Warning: Removed 1783 rows containing non-finite values (stat_smooth).## Warning: Removed 1783 rows containing non-finite values (stat_smooth).## Warning: Removed 1783 rows containing missing values (geom_point).## Warning: Removed 1783 rows containing non-finite values (stat_smooth).## Warning: Removed 1783 rows containing non-finite values (stat_smooth).## Warning: Removed 1783 rows containing missing values (geom_point).
## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 82.897 88.37 5.474
## 3 scrub.data 2 1 1 88.371 NA NAStep 2.1: scrub data
## [1] "numeric data missing in : "
## left_eye_center_x left_eye_center_y
## 1783 1783
## right_eye_center_x right_eye_center_y
## 1789 1789
## left_eye_inner_corner_x left_eye_inner_corner_y
## 6556 6556
## left_eye_outer_corner_x left_eye_outer_corner_y
## 6559 6559
## right_eye_inner_corner_x right_eye_inner_corner_y
## 6559 6559
## right_eye_outer_corner_x right_eye_outer_corner_y
## 6559 6559
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 6557 6557
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 6603 6603
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 6561 6561
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y
## 6594 6594
## nose_tip_x nose_tip_y
## 1783 1783
## mouth_left_corner_x mouth_left_corner_y
## 6562 6562
## mouth_right_corner_x mouth_right_corner_y
## 6561 6561
## mouth_center_top_lip_x mouth_center_top_lip_y
## 6557 6557
## mouth_center_bottom_lip_x mouth_center_bottom_lip_y
## 1816 1816
## [1] "numeric data w/ 0s in : "
## named integer(0)
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## ImageId
## 0## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 88.371 89.996 1.626
## 4 transform.data 2 2 2 89.997 NA NAStep 2.2: transform data
## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 89.997 90.037 0.04
## 5 extract.features 3 0 0 90.037 NA NAStep 3.0: extract features
## label step_major step_minor label_minor bgn end
## 5 extract.features 3 0 0 90.037 90.056
## 6 extract.features.string 3 1 1 90.057 NA
## elapsed
## 5 0.02
## 6 NAStep 3.1: extract features string
## label step_major step_minor label_minor bgn end
## 1 extract.features.string.bgn 1 0 0 90.086 NA
## elapsed
## 1 NA## label step_major step_minor
## 1 extract.features.string.bgn 1 0
## 2 extract.features.stringfactorize.str.vars 2 0
## label_minor bgn end elapsed
## 1 0 90.086 90.096 0.01
## 2 0 90.096 NA NA## .src ImageId
## ".src" "ImageId"## label step_major step_minor label_minor bgn
## 6 extract.features.string 3 1 1 90.057
## 7 extract.features.datetime 3 2 2 90.110
## end elapsed
## 6 90.109 0.053
## 7 NA NAStep 3.2: extract features datetime
## label step_major step_minor label_minor bgn
## 1 extract.features.datetime.bgn 1 0 0 90.136
## end elapsed
## 1 NA NA## label step_major step_minor label_minor bgn
## 7 extract.features.datetime 3 2 2 90.110
## 8 extract.features.price 3 3 3 90.146
## end elapsed
## 7 90.146 0.036
## 8 NA NAStep 3.3: extract features price
## label step_major step_minor label_minor bgn end
## 1 extract.features.price.bgn 1 0 0 90.177 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 8 extract.features.price 3 3 3 90.146 90.187
## 9 extract.features.text 3 4 4 90.187 NA
## elapsed
## 8 0.041
## 9 NAStep 3.4: extract features text
## label step_major step_minor label_minor bgn end
## 1 extract.features.text.bgn 1 0 0 90.231 NA
## elapsed
## 1 NA## label step_major step_minor label_minor bgn end
## 9 extract.features.text 3 4 4 90.187 90.241
## 10 extract.features.end 3 5 5 90.242 NA
## elapsed
## 9 0.054
## 10 NAStep 3.5: extract features end
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## label step_major step_minor label_minor bgn end
## 10 extract.features.end 3 5 5 90.242 91.147
## 11 manage.missing.data 4 0 0 91.147 NA
## elapsed
## 10 0.905
## 11 NAStep 4.0: manage missing data
## [1] "numeric data missing in : "
## left_eye_center_x left_eye_center_y
## 1783 1783
## right_eye_center_x right_eye_center_y
## 1789 1789
## left_eye_inner_corner_x left_eye_inner_corner_y
## 6556 6556
## left_eye_outer_corner_x left_eye_outer_corner_y
## 6559 6559
## right_eye_inner_corner_x right_eye_inner_corner_y
## 6559 6559
## right_eye_outer_corner_x right_eye_outer_corner_y
## 6559 6559
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 6557 6557
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 6603 6603
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 6561 6561
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y
## 6594 6594
## nose_tip_x nose_tip_y
## 1783 1783
## mouth_left_corner_x mouth_left_corner_y
## 6562 6562
## mouth_right_corner_x mouth_right_corner_y
## 6561 6561
## mouth_center_top_lip_x mouth_center_top_lip_y
## 6557 6557
## mouth_center_bottom_lip_x mouth_center_bottom_lip_y
## 1816 1816
## [1] "numeric data w/ 0s in : "
## named integer(0)
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## ImageId
## 0## [1] "numeric data missing in : "
## left_eye_center_x left_eye_center_y
## 1783 1783
## right_eye_center_x right_eye_center_y
## 1789 1789
## left_eye_inner_corner_x left_eye_inner_corner_y
## 6556 6556
## left_eye_outer_corner_x left_eye_outer_corner_y
## 6559 6559
## right_eye_inner_corner_x right_eye_inner_corner_y
## 6559 6559
## right_eye_outer_corner_x right_eye_outer_corner_y
## 6559 6559
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 6557 6557
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 6603 6603
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 6561 6561
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y
## 6594 6594
## nose_tip_x nose_tip_y
## 1783 1783
## mouth_left_corner_x mouth_left_corner_y
## 6562 6562
## mouth_right_corner_x mouth_right_corner_y
## 6561 6561
## mouth_center_top_lip_x mouth_center_top_lip_y
## 6557 6557
## mouth_center_bottom_lip_x mouth_center_bottom_lip_y
## 1816 1816
## [1] "numeric data w/ 0s in : "
## named integer(0)
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## ImageId
## 0## label step_major step_minor label_minor bgn end
## 11 manage.missing.data 4 0 0 91.147 91.526
## 12 cluster.data 5 0 0 91.527 NA
## elapsed
## 11 0.38
## 12 NAStep 5.0: cluster data
## label step_major step_minor label_minor bgn end
## 12 cluster.data 5 0 0 91.527 91.578
## 13 partition.data.training 6 0 0 91.579 NA
## elapsed
## 12 0.052
## 13 NAStep 6.0: partition data training
## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"## Loading required package: reshape2## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 0.18 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 0.18 secs"## Loading required package: sampling
##
## Attaching package: 'sampling'
##
## The following objects are masked from 'package:survival':
##
## cluster, strata
##
## The following object is masked from 'package:caret':
##
## cluster## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 0.43 secs"## .category .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 1 .dummy 5076 1963 1783 1 1
## .freqRatio.Tst
## 1 1## [1] "glbObsAll: "## [1] 8822 36## [1] "glbObsTrn: "## [1] 7039 36## [1] "glbObsFit: "## [1] 5076 35## [1] "glbObsOOB: "## [1] 1963 35## [1] "glbObsNew: "## [1] 1783 35## [1] "partition.data.training chunk: teardown: elapsed: 0.71 secs"## label step_major step_minor label_minor bgn end
## 13 partition.data.training 6 0 0 91.579 92.342
## 14 select.features 7 0 0 92.342 NA
## elapsed
## 13 0.763
## 14 NAStep 7.0: select features
## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## cor.y exclude.as.feat cor.y.abs
## left_eye_outer_corner_x 0.879976441 1 0.879976441
## left_eye_inner_corner_x 0.856572062 1 0.856572062
## left_eyebrow_outer_end_x 0.796637877 1 0.796637877
## left_eyebrow_inner_end_x 0.631805012 1 0.631805012
## nose_tip_x 0.457467547 1 0.457467547
## mouth_left_corner_x 0.422891342 1 0.422891342
## mouth_center_bottom_lip_x 0.374314178 1 0.374314178
## right_eye_inner_corner_x 0.308648848 1 0.308648848
## mouth_left_corner_y 0.284087076 1 0.284087076
## right_eye_center_x 0.274459196 1 0.274459196
## right_eyebrow_inner_end_x 0.268462848 1 0.268462848
## mouth_center_bottom_lip_y 0.265655182 1 0.265655182
## mouth_center_top_lip_x 0.260752995 1 0.260752995
## mouth_right_corner_y 0.202841552 1 0.202841552
## nose_tip_y 0.192274232 1 0.192274232
## mouth_center_top_lip_y 0.151302193 1 0.151302193
## left_eye_outer_corner_y 0.062128134 1 0.062128134
## .pos 0.053615179 0 0.053615179
## left_eye_center_y 0.029907546 1 0.029907546
## left_eye_inner_corner_y 0.022211192 1 0.022211192
## mouth_right_corner_x -0.008381762 1 0.008381762
## .rnorm -0.023380852 0 0.023380852
## right_eye_outer_corner_x -0.065015870 1 0.065015870
## left_eyebrow_outer_end_y -0.071141289 1 0.071141289
## right_eye_outer_corner_y -0.075857186 1 0.075857186
## right_eye_inner_corner_y -0.104697821 1 0.104697821
## left_eyebrow_inner_end_y -0.128192861 1 0.128192861
## right_eyebrow_outer_end_x -0.137145707 1 0.137145707
## right_eye_center_y -0.154727956 1 0.154727956
## right_eyebrow_outer_end_y -0.190547966 1 0.190547966
## right_eyebrow_inner_end_y -0.201853036 1 0.201853036
## .category NA 1 NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## left_eye_outer_corner_x NA 1.0 31.97897429 FALSE FALSE
## left_eye_inner_corner_x NA 1.0 32.07842023 FALSE FALSE
## left_eyebrow_outer_end_x NA 1.0 31.43912488 FALSE FALSE
## left_eyebrow_inner_end_x NA 1.0 32.10683336 FALSE FALSE
## nose_tip_x NA 1.0 99.55959653 FALSE FALSE
## mouth_left_corner_x NA 1.0 31.95056116 FALSE FALSE
## mouth_center_bottom_lip_x NA 1.0 98.99133400 FALSE FALSE
## right_eye_inner_corner_x NA 2.5 31.92214803 FALSE FALSE
## mouth_left_corner_y NA 1.0 31.96476772 FALSE FALSE
## right_eye_center_x NA 1.0 99.77269499 FALSE FALSE
## right_eyebrow_inner_end_x NA 1.0 31.99318085 FALSE FALSE
## mouth_center_bottom_lip_y NA 1.0 99.20443245 FALSE FALSE
## mouth_center_top_lip_x NA 1.0 32.06421367 FALSE FALSE
## mouth_right_corner_y NA 1.0 31.95056116 FALSE FALSE
## nose_tip_y NA 1.0 99.73007529 FALSE FALSE
## mouth_center_top_lip_y NA 1.0 32.06421367 FALSE FALSE
## left_eye_outer_corner_y NA 1.0 31.87952834 FALSE FALSE
## .pos NA 1.0 100.00000000 FALSE FALSE
## left_eye_center_y NA 1.0 99.68745560 FALSE FALSE
## left_eye_inner_corner_y NA 1.0 31.87952834 FALSE FALSE
## mouth_right_corner_x NA 1.0 31.97897429 FALSE FALSE
## .rnorm NA 1.0 100.00000000 FALSE FALSE
## right_eye_outer_corner_x NA 1.0 32.00738741 FALSE FALSE
## left_eyebrow_outer_end_y NA 1.5 31.36809206 FALSE FALSE
## right_eye_outer_corner_y NA 1.0 31.97897429 FALSE FALSE
## right_eye_inner_corner_y NA 1.0 31.93635460 FALSE FALSE
## left_eyebrow_inner_end_y NA 1.0 32.03580054 FALSE FALSE
## right_eyebrow_outer_end_x NA 1.0 31.59539707 FALSE FALSE
## right_eye_center_y NA 1.0 99.58800966 FALSE FALSE
## right_eyebrow_outer_end_y NA 1.0 31.58119051 FALSE FALSE
## right_eyebrow_inner_end_y NA 1.0 31.95056116 FALSE FALSE
## .category NA 0.0 0.01420656 TRUE TRUE
## is.cor.y.abs.low
## left_eye_outer_corner_x FALSE
## left_eye_inner_corner_x FALSE
## left_eyebrow_outer_end_x FALSE
## left_eyebrow_inner_end_x FALSE
## nose_tip_x FALSE
## mouth_left_corner_x FALSE
## mouth_center_bottom_lip_x FALSE
## right_eye_inner_corner_x FALSE
## mouth_left_corner_y FALSE
## right_eye_center_x FALSE
## right_eyebrow_inner_end_x FALSE
## mouth_center_bottom_lip_y FALSE
## mouth_center_top_lip_x FALSE
## mouth_right_corner_y FALSE
## nose_tip_y FALSE
## mouth_center_top_lip_y FALSE
## left_eye_outer_corner_y FALSE
## .pos FALSE
## left_eye_center_y FALSE
## left_eye_inner_corner_y TRUE
## mouth_right_corner_x TRUE
## .rnorm FALSE
## right_eye_outer_corner_x FALSE
## left_eyebrow_outer_end_y FALSE
## right_eye_outer_corner_y FALSE
## right_eye_inner_corner_y FALSE
## left_eyebrow_inner_end_y FALSE
## right_eyebrow_outer_end_x FALSE
## right_eye_center_y FALSE
## right_eyebrow_outer_end_y FALSE
## right_eyebrow_inner_end_y FALSE
## .category NA## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).## Warning: Removed 31 rows containing missing values (geom_point).
## cor.y exclude.as.feat cor.y.abs cor.high.X freqRatio
## .category NA 1 NA NA 0
## percentUnique zeroVar nzv is.cor.y.abs.low
## .category 0.01420656 TRUE TRUE NA
## [1] "numeric data missing in : "
## left_eye_center_x left_eye_center_y
## 1783 1783
## right_eye_center_x right_eye_center_y
## 1789 1789
## left_eye_inner_corner_x left_eye_inner_corner_y
## 6556 6556
## left_eye_outer_corner_x left_eye_outer_corner_y
## 6559 6559
## right_eye_inner_corner_x right_eye_inner_corner_y
## 6559 6559
## right_eye_outer_corner_x right_eye_outer_corner_y
## 6559 6559
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 6557 6557
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 6603 6603
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 6561 6561
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y
## 6594 6594
## nose_tip_x nose_tip_y
## 1783 1783
## mouth_left_corner_x mouth_left_corner_y
## 6562 6562
## mouth_right_corner_x mouth_right_corner_y
## 6561 6561
## mouth_center_top_lip_x mouth_center_top_lip_y
## 6557 6557
## mouth_center_bottom_lip_x mouth_center_bottom_lip_y
## 1816 1816
## [1] "numeric data w/ 0s in : "
## named integer(0)
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## ImageId .lcn
## 0 1783## [1] "glb_feats_df:"## [1] 32 12## id exclude.as.feat rsp_var
## left_eye_center_x left_eye_center_x TRUE TRUE## id cor.y exclude.as.feat cor.y.abs
## left_eye_center_x left_eye_center_x NA TRUE NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## left_eye_center_x NA NA NA NA NA
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## left_eye_center_x NA NA NA
## rsp_var_raw id_var rsp_var
## left_eye_center_x NA NA TRUE## [1] "glb_feats_df vs. glbObsAll: "## character(0)## [1] "glbObsAll vs. glb_feats_df: "## character(0)## label step_major step_minor label_minor bgn end elapsed
## 14 select.features 7 0 0 92.342 95.198 2.856
## 15 fit.models 8 0 0 95.198 NA NAStep 8.0: fit models
fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 95.74 NA NA# load(paste0(glb_out_pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glb_out_pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
c("id.prefix", "method", "type",
# trainControl params
"preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# train params
"metric", "metric.maximize", "tune.df")## [1] "id.prefix" "method" "type"
## [4] "preProc.method" "cv.n.folds" "cv.n.repeats"
## [7] "summary.fn" "metric" "metric.maximize"
## [10] "tune.df"# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indep_vars = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor bgn
## 1 fit.models_0_bgn 1 0 setup 95.740
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet 95.776
## end elapsed
## 1 95.775 0.035
## 2 NA NAret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indep_vars: .pos,.rnorm"## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-2## Fitting alpha = 0.1, lambda = 0.00376 on full training set
## Length Class Mode
## a0 61 -none- numeric
## beta 122 dgCMatrix S4
## df 61 -none- numeric
## dim 2 -none- numeric
## lambda 61 -none- numeric
## dev.ratio 61 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .pos .rnorm
## 6.604155e+01 9.251347e-05 -8.055063e-02
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" ".pos" ".rnorm"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet .pos,.rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.759 0.011 0.003546848
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 3.435571 0.003154002 0.003183645 3.458751 0.002166485if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indep_vars = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indep_vars: .pos,.rnorm"## Loading required package: rpart## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.00498 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 5076
##
## CP nsplit rel error
## 1 0.010138740 0 1.0000000
## 2 0.004984991 1 0.9898613
##
## Variable importance
## .pos .rnorm
## 99 1
##
## Node number 1: 5076 observations, complexity param=0.01013874
## mean=66.36569, MSE=11.84516
## left son=2 (2400 obs) right son=3 (2676 obs)
## Primary splits:
## .pos < 3326.5 to the left, improve=0.010138740, (0 missing)
## .rnorm < -1.918552 to the right, improve=0.001212581, (0 missing)
## Surrogate splits:
## .rnorm < -1.438368 to the left, agree=0.534, adj=0.014, (0 split)
##
## Node number 2: 2400 observations
## mean=65.99976, MSE=11.91095
##
## Node number 3: 2676 observations
## mean=66.69388, MSE=11.55836
##
## n= 5076
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 5076 60126.05 66.36569
## 2) .pos< 3326.5 2400 28586.28 65.99976 *
## 3) .pos>=3326.5 2676 30930.17 66.69388 *
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart .pos,.rnorm 5
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 1.724 0.041 0.01013874
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 3.43023 NA 0.009605254 3.447592 NA
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.008951045 0.09970171 0.00341234if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjust_interaction_feats(indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indep_vars = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")## label step_major step_minor label_minor bgn
## 2 fit.models_0_Max.cor.Y.rcv.*X* 1 1 glmnet 95.776
## 3 fit.models_0_Low.cor.X 1 2 glmnet 101.256
## end elapsed
## 2 101.256 5.48
## 3 NA NAindep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv &
(exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Low.cor.X",
type=glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indep_vars: .pos,.rnorm"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00376 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 61 -none- numeric
## beta 122 dgCMatrix S4
## df 61 -none- numeric
## dim 2 -none- numeric
## lambda 61 -none- numeric
## dev.ratio 61 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .pos .rnorm
## 6.604155e+01 9.251347e-05 -8.055063e-02
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" ".pos" ".rnorm"
## id feats max.nTuningRuns
## 1 Low.cor.X##rcv#glmnet .pos,.rnorm 20
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 2.065 0.006 0.003546848
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 3.435697 0.003154002 0.003183645 3.458751 0.002166485
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.003675718 0.1022751 0.002502483fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 3 fit.models_0_Low.cor.X 1 2 glmnet 101.256 104.931
## 4 fit.models_0_end 1 3 teardown 104.932 NA
## elapsed
## 3 3.675
## 4 NArm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 15 fit.models 8 0 0 95.198 104.95 9.752
## 16 fit.models 8 1 1 104.950 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor="setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 105.836 NA NA#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indep_vars <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjust_interaction_feats(myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
indep_vars <- setdiff(indep_vars, topindep_var)
if (length(interact_vars) > 0) {
indep_vars <-
setdiff(indep_vars, myextract_actual_feats(interact_vars))
indep_vars <- c(indep_vars,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indep_vars <- union(indep_vars, topindep_var)
}
}
if (is.null(indep_vars))
indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indep_vars))
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
if ((length(indep_vars) == 1) && (grepl("^%<d-%", indep_vars))) {
indep_vars <-
eval(parse(text = str_trim(unlist(strsplit(indep_vars, "%<d-%"))[2])))
}
indep_vars <- myadjust_interaction_feats(indep_vars)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars <- setdiff(indep_vars, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indep_vars]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indep_vars]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 105.836 105.847
## 2 fit.models_1_All.X 1 1 setup 105.847 NA
## elapsed
## 1 0.011
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 105.847 105.854
## 3 fit.models_1_All.X 1 2 glmnet 105.855 NA
## elapsed
## 2 0.007
## 3 NA
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indep_vars: .pos,.rnorm"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00376 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 61 -none- numeric
## beta 122 dgCMatrix S4
## df 61 -none- numeric
## dim 2 -none- numeric
## lambda 61 -none- numeric
## dev.ratio 61 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .pos .rnorm
## 6.604155e+01 9.251347e-05 -8.055063e-02
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" ".pos" ".rnorm"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 All.X##rcv#glmnet .pos,.rnorm 20 2.13
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit
## 1 0.007 0.003546848 3.435697 0.003154002
## max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB max.Rsquared.fit
## 1 0.003183645 3.458751 0.002166485 0.003675718
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1022751 0.002502483
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 105.855 109.517
## 4 fit.models_1_All.X 1 3 glm 109.517 NA
## elapsed
## 3 3.662
## 4 NA
## [1] "fitting model: All.X##rcv#glm"
## [1] " indep_vars: .pos,.rnorm"
## + Fold1.Rep1: parameter=none
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -31.0235 -1.2536 0.1418 1.6532 28.2569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.604e+01 9.644e-02 684.762 < 2e-16 ***
## .pos 9.304e-05 2.374e-05 3.920 8.98e-05 ***
## .rnorm -8.142e-02 4.809e-02 -1.693 0.0905 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 11.81013)
##
## Null deviance: 60126 on 5075 degrees of freedom
## Residual deviance: 59913 on 5073 degrees of freedom
## AIC: 26942
##
## Number of Fisher Scoring iterations: 2
##
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 All.X##rcv#glm .pos,.rnorm 1 1.154
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit min.aic.fit
## 1 0.01 0.003547006 3.435704 26942.49
## max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 0.00315416 0.003181723 3.458754 0.002164562
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.003676082 0.1022627 0.002501139# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 109.517 112.865
## 5 fit.models_1_preProc 1 4 preProc 112.865 NA
## elapsed
## 4 3.348
## 5 NAmdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id feats
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet .pos,.rnorm
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart .pos,.rnorm
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet .pos,.rnorm
## All.X##rcv#glmnet All.X##rcv#glmnet .pos,.rnorm
## All.X##rcv#glm All.X##rcv#glm .pos,.rnorm
## max.nTuningRuns min.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet 0 0.759
## Max.cor.Y##rcv#rpart 5 1.724
## Low.cor.X##rcv#glmnet 20 2.065
## All.X##rcv#glmnet 20 2.130
## All.X##rcv#glm 1 1.154
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit
## Max.cor.Y.rcv.1X1###glmnet 0.011 0.003546848 3.435571
## Max.cor.Y##rcv#rpart 0.041 0.010138740 3.430230
## Low.cor.X##rcv#glmnet 0.006 0.003546848 3.435697
## All.X##rcv#glmnet 0.007 0.003546848 3.435697
## All.X##rcv#glm 0.010 0.003547006 3.435704
## max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## Max.cor.Y.rcv.1X1###glmnet 0.003154002 0.003183645 3.458751
## Max.cor.Y##rcv#rpart NA 0.009605254 3.447592
## Low.cor.X##rcv#glmnet 0.003154002 0.003183645 3.458751
## All.X##rcv#glmnet 0.003154002 0.003183645 3.458751
## All.X##rcv#glm 0.003154160 0.003181723 3.458754
## max.Adj.R.sq.OOB max.Rsquared.fit
## Max.cor.Y.rcv.1X1###glmnet 0.002166485 NA
## Max.cor.Y##rcv#rpart NA 0.008951045
## Low.cor.X##rcv#glmnet 0.002166485 0.003675718
## All.X##rcv#glmnet 0.002166485 0.003675718
## All.X##rcv#glm 0.002164562 0.003676082
## min.RMSESD.fit max.RsquaredSD.fit min.aic.fit
## Max.cor.Y.rcv.1X1###glmnet NA NA NA
## Max.cor.Y##rcv#rpart 0.09970171 0.003412340 NA
## Low.cor.X##rcv#glmnet 0.10227511 0.002502483 NA
## All.X##rcv#glmnet 0.10227511 0.002502483 NA
## All.X##rcv#glm 0.10226265 0.002501139 26942.49rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 112.865 112.926
## 6 fit.models_1_end 1 5 teardown 112.927 NA
## elapsed
## 5 0.062
## 6 NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)## label step_major step_minor label_minor bgn end elapsed
## 16 fit.models 8 1 1 104.950 112.936 7.986
## 17 fit.models 8 2 2 112.936 NA NAfit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 114.862 NA NAplt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id feats
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet .pos,.rnorm
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart .pos,.rnorm
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet .pos,.rnorm
## All.X##rcv#glmnet All.X##rcv#glmnet .pos,.rnorm
## All.X##rcv#glm All.X##rcv#glm .pos,.rnorm
## max.nTuningRuns max.R.sq.fit max.Adj.R.sq.fit
## Max.cor.Y.rcv.1X1###glmnet 0 0.003546848 0.003154002
## Max.cor.Y##rcv#rpart 5 0.010138740 NA
## Low.cor.X##rcv#glmnet 20 0.003546848 0.003154002
## All.X##rcv#glmnet 20 0.003546848 0.003154002
## All.X##rcv#glm 1 0.003547006 0.003154160
## max.R.sq.OOB max.Adj.R.sq.OOB max.Rsquared.fit
## Max.cor.Y.rcv.1X1###glmnet 0.003183645 0.002166485 NA
## Max.cor.Y##rcv#rpart 0.009605254 NA 0.008951045
## Low.cor.X##rcv#glmnet 0.003183645 0.002166485 0.003675718
## All.X##rcv#glmnet 0.003183645 0.002166485 0.003675718
## All.X##rcv#glm 0.003181723 0.002164562 0.003676082
## inv.elapsedtime.everything
## Max.cor.Y.rcv.1X1###glmnet 1.3175231
## Max.cor.Y##rcv#rpart 0.5800464
## Low.cor.X##rcv#glmnet 0.4842615
## All.X##rcv#glmnet 0.4694836
## All.X##rcv#glm 0.8665511
## inv.elapsedtime.final inv.RMSE.fit inv.RMSE.OOB
## Max.cor.Y.rcv.1X1###glmnet 90.90909 0.2910724 0.2891217
## Max.cor.Y##rcv#rpart 24.39024 0.2915257 0.2900575
## Low.cor.X##rcv#glmnet 166.66667 0.2910617 0.2891217
## All.X##rcv#glmnet 142.85714 0.2910617 0.2891217
## All.X##rcv#glm 100.00000 0.2910612 0.2891215
## inv.aic.fit
## Max.cor.Y.rcv.1X1###glmnet NA
## Max.cor.Y##rcv#rpart NA
## Low.cor.X##rcv#glmnet NA
## All.X##rcv#glmnet NA
## All.X##rcv#glm 3.711609e-05# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])## id min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## 2 Max.cor.Y##rcv#rpart 3.447592 0.009605254 NA
## 1 Max.cor.Y.rcv.1X1###glmnet 3.458751 0.003183645 0.003154002
## 3 Low.cor.X##rcv#glmnet 3.458751 0.003183645 0.003154002
## 4 All.X##rcv#glmnet 3.458751 0.003183645 0.003154002
## 5 All.X##rcv#glm 3.458754 0.003181723 0.003154160
## min.RMSE.fit
## 2 3.430230
## 1 3.435571
## 3 3.435697
## 4 3.435697
## 5 3.435704# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fit + min.RMSE.fit
## <environment: 0x7ffb568076a8>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: Max.cor.Y##rcv#rpart"glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indep_vars <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indep_vars <- paste(indep_vars, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indep_vars <- intersect(indep_vars, names(glbObsFit))
# indep_vars <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
# if (glb_is_classification && glb_is_binomial)
# indep_vars <- grep("prob$", indep_vars, value=TRUE) else
# indep_vars <- indep_vars[!grepl("err$", indep_vars)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indep_vars)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indep_vars = indep_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id)) ## [1] "User specified selection: All.X##rcv#glmnet"myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 61 -none- numeric
## beta 122 dgCMatrix S4
## df 61 -none- numeric
## dim 2 -none- numeric
## lambda 61 -none- numeric
## dev.ratio 61 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .pos .rnorm
## 6.604155e+01 9.251347e-05 -8.055063e-02
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" ".pos" ".rnorm"## [1] TRUE# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet fit prediction diagnostics:"glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glb_sel_mdl_id,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))## All.X..rcv.glmnet.imp imp All.X##rcv#glmnet.imp
## .pos 100 100 100
## .rnorm 0 0 0#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
prob_threshold = glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id) 
## left_eye_center_x left_eye_center_y right_eye_center_x
## 1908 22.76334 55.61720 1.528527
## 6406 37.95218 45.91296 30.872141
## 6316 38.85352 36.18525 3.672231
## 1621 40.45900 40.76164 NA
## 2192 41.03546 37.92189 12.779046
## right_eye_center_y left_eye_inner_corner_x left_eye_inner_corner_y
## 1908 56.40497 19.06495 56.29124
## 6406 45.09604 NA NA
## 6316 30.39885 NA NA
## 1621 NA 35.55209 39.43738
## 2192 41.72817 32.98262 40.65430
## left_eye_outer_corner_x left_eye_outer_corner_y
## 1908 27.57188 56.38438
## 6406 NA NA
## 6316 NA NA
## 1621 46.61831 42.57863
## 2192 48.63092 37.10972
## right_eye_inner_corner_x right_eye_inner_corner_y
## 1908 5.751046 56.74390
## 6406 NA NA
## 6316 NA NA
## 1621 NA NA
## 2192 18.194942 42.77734
## right_eye_outer_corner_x right_eye_outer_corner_y
## 1908 NA NA
## 6406 NA NA
## 6316 NA NA
## 1621 NA NA
## 2192 4.424137 42.36508
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 1908 17.88872 51.04292
## 6406 NA NA
## 6316 NA NA
## 1621 28.91893 34.32552
## 2192 24.27327 33.97577
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 1908 32.20643 49.89555
## 6406 NA NA
## 6316 NA NA
## 1621 50.21866 36.25489
## 2192 54.04693 30.76984
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 1908 6.921014 51.24334
## 6406 NA NA
## 6316 NA NA
## 1621 26.095147 33.85276
## 2192 12.013533 37.27076
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y nose_tip_x
## 1908 NA NA 12.94470
## 6406 NA NA 34.13983
## 6316 NA NA 15.24502
## 1621 23.8719 32.92002 19.53418
## 2192 NA NA 20.51485
## nose_tip_y mouth_left_corner_x mouth_left_corner_y
## 1908 60.06969 22.92336 73.48394
## 6406 51.26845 NA NA
## 6316 63.26558 NA NA
## 1621 55.67276 NA NA
## 2192 64.67113 47.65531 75.03647
## mouth_right_corner_x mouth_right_corner_y mouth_center_top_lip_x
## 1908 2.245766 74.12832 12.60517
## 6406 NA NA NA
## 6316 NA NA NA
## 1621 NA NA 29.32441
## 2192 20.800524 79.31402 28.80664
## mouth_center_top_lip_y mouth_center_bottom_lip_x
## 1908 71.88882 12.53648
## 6406 NA 34.13983
## 6316 NA 18.25395
## 1621 66.69852 29.32441
## 2192 81.62436 28.85238
## mouth_center_bottom_lip_y .src ImageId .rnorm .pos .category
## 1908 76.62886 Train Train#1908 -0.3524003 1908 .dummy
## 6406 54.71770 Train Train#6406 -1.2134735 6406 .dummy
## 6316 75.06980 Train Train#6316 -0.6224332 6316 .dummy
## 1621 72.60010 Train Train#1621 0.9903102 1621 .dummy
## 2192 83.08833 Train Train#2192 -0.3039093 2192 .dummy
## left_eye_center_x.All.X..rcv.glmnet
## 1908 66.24646
## 6406 66.73194
## 6316 66.67601
## 1621 66.11175
## 2192 66.26882
## left_eye_center_x.All.X..rcv.glmnet.err
## 1908 43.48311
## 6406 28.77976
## 6316 27.82248
## 1621 25.65275
## 2192 25.23336
## left_eye_center_x.All.X..rcv.glmnet.err.abs
## 1908 43.48311
## 6406 28.77976
## 6316 27.82248
## 1621 25.65275
## 2192 25.23336
## left_eye_center_x.All.X..rcv.glmnet.is.acc .label
## 1908 FALSE Train#1908
## 6406 FALSE Train#6406
## 6316 FALSE Train#6316
## 1621 FALSE Train#1621
## 2192 FALSE Train#2192
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glb_sel_mdl_id,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glb_sel_mdl_id,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}## .category .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## .dummy .dummy 1963 5076 1783 1 1
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit
## .dummy 1 10653.64 2.098825 5076
## err.abs.OOB.sum err.abs.OOB.mean
## .dummy 4143.806 2.110956
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 1963.000000 5076.000000 1783.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 10653.635212 2.098825
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 5076.000000 4143.806047 2.110956write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 120.722 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 17 fit.models 8 2 2 112.936 120.731 7.795
## 18 fit.models 8 3 3 120.732 NA NA# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glbObsAll, #glbObsTrn, glbObsFit, glbObsOOB, glbObsNew,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 18 fit.models 8 3 3 120.732 125.088
## 19 fit.data.training 9 0 0 125.089 NA
## elapsed
## 18 4.356
## 19 NAStep 9.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indep_vars <- myadjust_interaction_feats(subset(glb_feats_df,
!nzv & (exclude.as.feat != 1))[, "id"])
rfe_trn_results <-
myrun_rfe(glbObsTrn, indep_vars, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjust_interaction_feats(myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indep_vars = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glb_sel_mdl_id, fixed = TRUE)) {
indep_vars_vctr <- myextract_actual_feats(predictors(rfe_trn_results))
} else indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glb_sel_mdl_id),
"Final.Ensemble", "Final")
trnobs_df <- if (is.null(glbObsTrnOutliers[[mdl_id_pfx]])) glbObsTrn else
glbObsTrn[!(glbObsTrn[, glbFeatsId] %in%
glbObsTrnOutliers[[mdl_id_pfx]]), ]
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glb_sel_mdl_id)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indep_vars_vctr) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indep_vars = indep_vars_vctr, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
}
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}## [1] "fitting model: Final##rcv#glmnet"
## [1] " indep_vars: .pos,.rnorm"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0172 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 61 -none- numeric
## beta 122 dgCMatrix S4
## df 61 -none- numeric
## dim 2 -none- numeric
## lambda 61 -none- numeric
## dev.ratio 61 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) .pos .rnorm
## 6.604293e+01 8.993199e-05 -7.983577e-02
## [1] "max lambda < lambdaOpt:"
## (Intercept) .pos .rnorm
## 6.604253e+01 9.004627e-05 -8.002579e-02
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 Final##rcv#glmnet .pos,.rnorm 20 2.316
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit
## 1 0.008 0.003445728 3.441975 0.003162455
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.002829642 0.1253077 0.001332354rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 19 fit.data.training 9 0 0 125.089 129.222
## 20 fit.data.training 9 1 1 129.223 NA
## elapsed
## 19 4.133
## 20 NA#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glb_fin_mdl_id)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glb_fin_mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)## All.X..rcv.glmnet.imp All.X##rcv#glmnet.imp Final..rcv.glmnet.imp
## .pos 100 100 100
## .rnorm 0 0 0
## imp Final##rcv#glmnet.imp
## .pos 100 100
## .rnorm 0 0if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glb_fin_mdl_id) 
## left_eye_center_x left_eye_center_y right_eye_center_x
## 1908 22.76334 55.61720 1.528527
## 2788 35.34845 35.72961 10.797496
## 1862 36.16566 39.99390 23.196134
## 6493 36.94935 44.27019 34.291798
## 6406 37.95218 45.91296 30.872141
## right_eye_center_y left_eye_inner_corner_x left_eye_inner_corner_y
## 1908 56.40497 19.06495 56.29124
## 2788 48.00511 NA NA
## 1862 43.81474 34.14098 40.55803
## 6493 43.88362 NA NA
## 6406 45.09604 NA NA
## left_eye_outer_corner_x left_eye_outer_corner_y
## 1908 27.57188 56.38438
## 2788 NA NA
## 1862 40.60971 38.05205
## 6493 NA NA
## 6406 NA NA
## right_eye_inner_corner_x right_eye_inner_corner_y
## 1908 5.751046 56.74390
## 2788 NA NA
## 1862 25.041023 43.77863
## 6493 NA NA
## 6406 NA NA
## right_eye_outer_corner_x right_eye_outer_corner_y
## 1908 NA NA
## 2788 NA NA
## 1862 22.04996 43.9963
## 6493 NA NA
## 6406 NA NA
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 1908 17.88872 51.04292
## 2788 NA NA
## 1862 24.54988 37.64245
## 6493 NA NA
## 6406 NA NA
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 1908 32.20643 49.89555
## 2788 NA NA
## 1862 43.18563 32.53290
## 6493 NA NA
## 6406 NA NA
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 1908 6.921014 51.24334
## 2788 NA NA
## 1862 22.136163 42.76767
## 6493 NA NA
## 6406 NA NA
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y nose_tip_x
## 1908 NA NA 12.94470
## 2788 NA NA 28.28077
## 1862 18.1551 40.9182 21.35837
## 6493 NA NA 35.40312
## 6406 NA NA 34.13983
## nose_tip_y mouth_left_corner_x mouth_left_corner_y
## 1908 60.06969 22.92336 73.48394
## 2788 60.28059 NA NA
## 1862 66.08490 37.14547 78.11135
## 6493 46.20297 NA NA
## 6406 51.26845 NA NA
## mouth_right_corner_x mouth_right_corner_y mouth_center_top_lip_x
## 1908 2.245766 74.12832 12.60517
## 2788 NA NA NA
## 1862 NA NA 29.67576
## 6493 NA NA NA
## 6406 NA NA NA
## mouth_center_top_lip_y mouth_center_bottom_lip_x
## 1908 71.88882 12.53648
## 2788 NA 34.97656
## 1862 78.16098 33.16245
## 6493 NA 35.40312
## 6406 NA 34.13983
## mouth_center_bottom_lip_y .src ImageId .rnorm .pos .lcn
## 1908 76.62886 Train Train#1908 -0.3524003 1908 OOB
## 2788 72.55607 Train Train#2788 -0.8952096 2788 Fit
## 1862 82.33012 Train Train#1862 0.6062188 1862 Fit
## 6493 47.21769 Train Train#6493 0.7944260 6493 Fit
## 6406 54.71770 Train Train#6406 -1.2134735 6406 OOB
## .category left_eye_center_x.All.X..rcv.glmnet
## 1908 .dummy NA
## 2788 .dummy 66.37159
## 1862 .dummy 66.16498
## 6493 .dummy 66.57825
## 6406 .dummy NA
## left_eye_center_x.All.X..rcv.glmnet.err
## 1908 NA
## 2788 31.02314
## 1862 29.99932
## 6493 29.62890
## 6406 NA
## left_eye_center_x.All.X..rcv.glmnet.err.abs
## 1908 NA
## 2788 31.02314
## 1862 29.99932
## 6493 29.62890
## 6406 NA
## left_eye_center_x.All.X..rcv.glmnet.is.acc
## 1908 NA
## 2788 FALSE
## 1862 FALSE
## 6493 FALSE
## 6406 NA
## left_eye_center_x.Final..rcv.glmnet
## 1908 66.24262
## 2788 66.36516
## 1862 66.16190
## 6493 66.56350
## 6406 66.71609
## left_eye_center_x.Final..rcv.glmnet.err
## 1908 43.47928
## 2788 31.01671
## 1862 29.99624
## 6493 29.61414
## 6406 28.76391
## left_eye_center_x.Final..rcv.glmnet.err.abs
## 1908 43.47928
## 2788 31.01671
## 1862 29.99624
## 6493 29.61414
## 6406 28.76391
## left_eye_center_x.Final..rcv.glmnet.is.acc .label
## 1908 FALSE Train#1908
## 2788 FALSE Train#2788
## 1862 FALSE Train#1862
## 6493 FALSE Train#6493
## 6406 FALSE Train#6406
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))## [1] "left_eye_center_x.Final..rcv.glmnet"
## [2] "left_eye_center_x.Final..rcv.glmnet.err"
## [3] "left_eye_center_x.Final..rcv.glmnet.err.abs"
## [4] "left_eye_center_x.Final..rcv.glmnet.is.acc"for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))## character(0)print(setdiff(names(glbObsOOB), names(glbObsAll)))## character(0)for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))## character(0)if (glb_save_envir)
save(glb_feats_df, glbObsAll,
#glbObsTrn, glbObsFit, glbObsOOB, glbObsNew,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file = paste0(glb_out_pfx, "dsk.RData"))
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glb_out_pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)## label step_major step_minor label_minor bgn end
## 20 fit.data.training 9 1 1 129.223 139.451
## 21 predict.data.new 10 0 0 139.452 NA
## elapsed
## 20 10.229
## 21 NAStep 10.0: predict data new
## Warning: Removed 1783 rows containing missing values (geom_point).## Warning: Removed 1783 rows containing missing values (geom_point).
## Warning: Removed 1783 rows containing missing values (geom_point).## Warning: Removed 1783 rows containing missing values (geom_point).
## left_eye_center_x left_eye_center_y right_eye_center_x
## 7050 NA NA NA
## 7051 NA NA NA
## 7052 NA NA NA
## 7053 NA NA NA
## 7054 NA NA NA
## right_eye_center_y left_eye_inner_corner_x left_eye_inner_corner_y
## 7050 NA NA NA
## 7051 NA NA NA
## 7052 NA NA NA
## 7053 NA NA NA
## 7054 NA NA NA
## left_eye_outer_corner_x left_eye_outer_corner_y
## 7050 NA NA
## 7051 NA NA
## 7052 NA NA
## 7053 NA NA
## 7054 NA NA
## right_eye_inner_corner_x right_eye_inner_corner_y
## 7050 NA NA
## 7051 NA NA
## 7052 NA NA
## 7053 NA NA
## 7054 NA NA
## right_eye_outer_corner_x right_eye_outer_corner_y
## 7050 NA NA
## 7051 NA NA
## 7052 NA NA
## 7053 NA NA
## 7054 NA NA
## left_eyebrow_inner_end_x left_eyebrow_inner_end_y
## 7050 NA NA
## 7051 NA NA
## 7052 NA NA
## 7053 NA NA
## 7054 NA NA
## left_eyebrow_outer_end_x left_eyebrow_outer_end_y
## 7050 NA NA
## 7051 NA NA
## 7052 NA NA
## 7053 NA NA
## 7054 NA NA
## right_eyebrow_inner_end_x right_eyebrow_inner_end_y
## 7050 NA NA
## 7051 NA NA
## 7052 NA NA
## 7053 NA NA
## 7054 NA NA
## right_eyebrow_outer_end_x right_eyebrow_outer_end_y nose_tip_x
## 7050 NA NA NA
## 7051 NA NA NA
## 7052 NA NA NA
## 7053 NA NA NA
## 7054 NA NA NA
## nose_tip_y mouth_left_corner_x mouth_left_corner_y
## 7050 NA NA NA
## 7051 NA NA NA
## 7052 NA NA NA
## 7053 NA NA NA
## 7054 NA NA NA
## mouth_right_corner_x mouth_right_corner_y mouth_center_top_lip_x
## 7050 NA NA NA
## 7051 NA NA NA
## 7052 NA NA NA
## 7053 NA NA NA
## 7054 NA NA NA
## mouth_center_top_lip_y mouth_center_bottom_lip_x
## 7050 NA NA
## 7051 NA NA
## 7052 NA NA
## 7053 NA NA
## 7054 NA NA
## mouth_center_bottom_lip_y .src ImageId .rnorm .pos .lcn
## 7050 NA Test Test#0001 0.1777388 7050
## 7051 NA Test Test#0002 -0.5182230 7051
## 7052 NA Test Test#0003 -0.5757552 7052
## 7053 NA Test Test#0004 0.6224964 7053
## 7054 NA Test Test#0005 1.0077015 7054
## .category left_eye_center_x.Final..rcv.glmnet
## 7050 .dummy 66.66288
## 7051 .dummy 66.71857
## 7052 .dummy 66.72326
## 7053 .dummy 66.62762
## 7054 .dummy 66.59693
## left_eye_center_x.Final..rcv.glmnet.err
## 7050 NA
## 7051 NA
## 7052 NA
## 7053 NA
## 7054 NA
## left_eye_center_x.Final..rcv.glmnet.err.abs
## 7050 NA
## 7051 NA
## 7052 NA
## 7053 NA
## 7054 NA
## left_eye_center_x.Final..rcv.glmnet.is.acc .label
## 7050 NA Test#0001
## 7051 NA Test#0002
## 7052 NA Test#0003
## 7053 NA Test#0004
## 7054 NA Test#0005
## Loading required package: stringr## [1] "glb_sel_mdl_id: All.X##rcv#glmnet"## [1] "glb_fin_mdl_id: Final##rcv#glmnet"## [1] "Cross Validation issues:"
## Max.cor.Y.rcv.1X1###glmnet
## 0## min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## Max.cor.Y##rcv#rpart 3.447592 0.009605254 NA
## Max.cor.Y.rcv.1X1###glmnet 3.458751 0.003183645 0.003154002
## Low.cor.X##rcv#glmnet 3.458751 0.003183645 0.003154002
## All.X##rcv#glmnet 3.458751 0.003183645 0.003154002
## All.X##rcv#glm 3.458754 0.003181723 0.003154160
## Final##rcv#glmnet NA NA 0.003162455
## min.RMSE.fit
## Max.cor.Y##rcv#rpart 3.430230
## Max.cor.Y.rcv.1X1###glmnet 3.435571
## Low.cor.X##rcv#glmnet 3.435697
## All.X##rcv#glmnet 3.435697
## All.X##rcv#glm 3.435704
## Final##rcv#glmnet 3.441975## [1] "All.X##rcv#glmnet OOB RMSE: 3.4588"## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.OOB .n.Tst
## .dummy 1 1 1 5076 1963 1783
## .n.fit .n.new .n.trn err.abs.OOB.mean err.abs.fit.mean
## .dummy 5076 1783 7039 2.110956 2.098825
## err.abs.new.mean err.abs.trn.mean err.abs.OOB.sum err.abs.fit.sum
## .dummy NA 2.102821 4143.806 10653.64
## err.abs.new.sum err.abs.trn.sum
## .dummy NA 14801.76
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 5076.000000
## .n.OOB .n.Tst .n.fit .n.new
## 1963.000000 1783.000000 5076.000000 1783.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 7039.000000 2.110956 2.098825 NA
## err.abs.trn.mean err.abs.OOB.sum err.abs.fit.sum err.abs.new.sum
## 2.102821 4143.806047 10653.635212 NA
## err.abs.trn.sum
## 14801.757870
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.OOB .n.Tst
## .dummy 1 1 1 5076 1963 1783
## .n.fit .n.new.x .n.new.y .n.trn err.abs.OOB.mean err.abs.fit.mean
## .dummy 5076 1783 1783 7039 2.110956 2.098825
## err.abs.trn.mean err.abs.new.mean.x err.abs.new.mean.y
## .dummy 2.102821 NA NA
## err.abs.OOB.sum err.abs.fit.sum err.abs.trn.sum err.abs.new.sum.x
## .dummy 4143.806 10653.64 14801.76 NA
## err.abs.new.sum.y
## .dummy NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst
## 1.000000 1.000000 1.000000
## .n.Fit .n.OOB .n.Tst
## 5076.000000 1963.000000 1783.000000
## .n.fit .n.new.x .n.new.y
## 5076.000000 1783.000000 1783.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean
## 7039.000000 2.110956 2.098825
## err.abs.trn.mean err.abs.new.mean.x err.abs.new.mean.y
## 2.102821 NA NA
## err.abs.OOB.sum err.abs.fit.sum err.abs.trn.sum
## 4143.806047 10653.635212 14801.757870
## err.abs.new.sum.x err.abs.new.sum.y
## NA NA## All.X..rcv.glmnet.imp All.X__rcv_glmnet.imp Final..rcv.glmnet.imp
## .pos 100 100 100
## Final__rcv_glmnet.imp
## .pos 100## [1] "glbObsNew prediction stats:"## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## label step_major step_minor label_minor bgn end
## 21 predict.data.new 10 0 0 139.452 154.481
## 22 display.session.info 11 0 0 154.482 NA
## elapsed
## 21 15.029
## 22 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 1 import.data 1 0 0 10.714
## 21 predict.data.new 10 0 0 139.452
## 20 fit.data.training 9 1 1 129.223
## 15 fit.models 8 0 0 95.198
## 16 fit.models 8 1 1 104.950
## 17 fit.models 8 2 2 112.936
## 2 inspect.data 2 0 0 82.897
## 18 fit.models 8 3 3 120.732
## 19 fit.data.training 9 0 0 125.089
## 14 select.features 7 0 0 92.342
## 3 scrub.data 2 1 1 88.371
## 10 extract.features.end 3 5 5 90.242
## 13 partition.data.training 6 0 0 91.579
## 11 manage.missing.data 4 0 0 91.147
## 9 extract.features.text 3 4 4 90.187
## 6 extract.features.string 3 1 1 90.057
## 12 cluster.data 5 0 0 91.527
## 8 extract.features.price 3 3 3 90.146
## 4 transform.data 2 2 2 89.997
## 7 extract.features.datetime 3 2 2 90.110
## 5 extract.features 3 0 0 90.037
## end elapsed duration
## 1 82.897 72.183 72.183
## 21 154.481 15.029 15.029
## 20 139.451 10.229 10.228
## 15 104.950 9.752 9.752
## 16 112.936 7.986 7.986
## 17 120.731 7.795 7.795
## 2 88.370 5.474 5.473
## 18 125.088 4.356 4.356
## 19 129.222 4.133 4.133
## 14 95.198 2.856 2.856
## 3 89.996 1.626 1.625
## 10 91.147 0.905 0.905
## 13 92.342 0.763 0.763
## 11 91.526 0.380 0.379
## 9 90.241 0.054 0.054
## 6 90.109 0.053 0.052
## 12 91.578 0.052 0.051
## 8 90.187 0.041 0.041
## 4 90.037 0.040 0.040
## 7 90.146 0.036 0.036
## 5 90.056 0.020 0.019
## [1] "Total Elapsed Time: 154.481 secs"